Imitation learning is a promising approach to end-to-end training of autonomous vehicle controllers. Typically the driving process with such approaches is entirely automatic and black-box, although in practice it is desirable to control the vehicle through high-level commands, such as telling it which way to go at an intersection. In existing work this has been accomplished by the application of a branched neural architecture, since directly providing the command as an additional input to the controller often results in the command being ignored. In this work we overcome this limitation by learning a disentangled probabilistic latent variable model that generates the steering commands. We achieve faithful command-conditional generation without using a branched architecture and demonstrate improved stability of the controller, applying only a variational objective without any domain-specific adjustments. On top of that, we extend our model with an additional latent variable and augment the dataset to train a controller that is robust to unsafe commands, such as asking it to turn into a wall. The main contribution of this work is a recipe for building controllable imitation driving agents that improves upon multiple aspects of the current state of the art relating to robustness and interpretability.
翻译:光学学习是自动汽车控制器端到端培训的一种很有希望的方法。 使用这种方法的驾驶过程通常是完全自动的和黑箱的,尽管在实践中,最好通过高级命令来控制车辆,例如告诉车辆在交叉点的方向。 在现有的工作中,这项工作是通过应用一个分形神经结构完成的,因为直接向控制器提供指令作为附加输入,往往导致命令被忽视。 在这项工作中,我们通过学习产生方向指令的分解的不稳定性潜在变量模型克服了这一限制。 我们实现了忠实的指令-条件生成,没有使用分支结构,并展示了控制器的稳定性得到改善,只应用了一个变异性目标,而没有任何特定域的调整。此外,我们扩展了我们的模型,增加了一个潜在的变量,并增加了数据集,以训练一个对不安全的指令具有坚固性的控制器,例如要求它转成一堵墙。 这项工作的主要贡献是建立可控性仿真剂,在与坚固性和解释性有关的当前艺术的多个方面有所改进。